Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations2363
Missing cells386
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory335.9 KiB
Average record size in memory145.6 B

Variable types

Text1
Numeric10

Alerts

Freedom to make life choices is highly overall correlated with Life Ladder and 1 other fieldsHigh correlation
Healthy life expectancy at birth is highly overall correlated with Life Ladder and 2 other fieldsHigh correlation
Life Ladder is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Log GDP per capita is highly overall correlated with Healthy life expectancy at birth and 2 other fieldsHigh correlation
Positive affect is highly overall correlated with Freedom to make life choices and 1 other fieldsHigh correlation
Social support is highly overall correlated with Healthy life expectancy at birth and 2 other fieldsHigh correlation
Log GDP per capita has 28 (1.2%) missing values Missing
Healthy life expectancy at birth has 63 (2.7%) missing values Missing
Freedom to make life choices has 36 (1.5%) missing values Missing
Generosity has 81 (3.4%) missing values Missing
Perceptions of corruption has 125 (5.3%) missing values Missing
Positive affect has 24 (1.0%) missing values Missing

Reproduction

Analysis started2024-12-11 17:27:07.045605
Analysis finished2024-12-11 17:27:29.687167
Duration22.64 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Country name
Text

Distinct165
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size151.3 KiB
2024-12-11T22:57:30.122011image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length25
Median length22
Mean length8.2526449
Min length4

Characters and Unicode

Total characters19501
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 52
 
1.8%
china 46
 
1.6%
of 45
 
1.6%
south 40
 
1.4%
congo 24
 
0.8%
republic 23
 
0.8%
and 21
 
0.7%
chile 18
 
0.6%
bolivia 18
 
0.6%
bangladesh 18
 
0.6%
Other values (178) 2543
89.3%
2024-12-11T22:57:30.877971image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3096
15.9%
i 1713
 
8.8%
n 1591
 
8.2%
e 1298
 
6.7%
o 1109
 
5.7%
r 1075
 
5.5%
t 717
 
3.7%
l 700
 
3.6%
s 583
 
3.0%
u 571
 
2.9%
Other values (45) 7048
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3096
15.9%
i 1713
 
8.8%
n 1591
 
8.2%
e 1298
 
6.7%
o 1109
 
5.7%
r 1075
 
5.5%
t 717
 
3.7%
l 700
 
3.6%
s 583
 
3.0%
u 571
 
2.9%
Other values (45) 7048
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3096
15.9%
i 1713
 
8.8%
n 1591
 
8.2%
e 1298
 
6.7%
o 1109
 
5.7%
r 1075
 
5.5%
t 717
 
3.7%
l 700
 
3.6%
s 583
 
3.0%
u 571
 
2.9%
Other values (45) 7048
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3096
15.9%
i 1713
 
8.8%
n 1591
 
8.2%
e 1298
 
6.7%
o 1109
 
5.7%
r 1075
 
5.5%
t 717
 
3.7%
l 700
 
3.6%
s 583
 
3.0%
u 571
 
2.9%
Other values (45) 7048
36.1%

year
Real number (ℝ)

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.7639
Minimum2005
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:31.157482image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2007
Q12011
median2015
Q32019
95-th percentile2023
Maximum2023
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0594365
Coefficient of variation (CV)0.0025111809
Kurtosis-1.0891015
Mean2014.7639
Median Absolute Deviation (MAD)4
Skewness-0.064369054
Sum4760887
Variance25.597897
MonotonicityNot monotonic
2024-12-11T22:57:31.389787image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2017 147
 
6.2%
2011 146
 
6.2%
2014 144
 
6.1%
2019 143
 
6.1%
2015 142
 
6.0%
2018 141
 
6.0%
2016 141
 
6.0%
2012 141
 
6.0%
2022 140
 
5.9%
2023 138
 
5.8%
Other values (9) 940
39.8%
ValueCountFrequency (%)
2005 27
 
1.1%
2006 89
3.8%
2007 102
4.3%
2008 110
4.7%
2009 114
4.8%
2010 124
5.2%
2011 146
6.2%
2012 141
6.0%
2013 136
5.8%
2014 144
6.1%
ValueCountFrequency (%)
2023 138
5.8%
2022 140
5.9%
2021 122
5.2%
2020 116
4.9%
2019 143
6.1%
2018 141
6.0%
2017 147
6.2%
2016 141
6.0%
2015 142
6.0%
2014 144
6.1%

Life Ladder
Real number (ℝ)

High correlation 

Distinct1814
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4835658
Minimum1.281
Maximum8.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:31.678481image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1.281
5-th percentile3.6612
Q14.647
median5.449
Q36.3235
95-th percentile7.3638
Maximum8.019
Range6.738
Interquartile range (IQR)1.6765

Descriptive statistics

Standard deviation1.1255215
Coefficient of variation (CV)0.20525358
Kurtosis-0.56227035
Mean5.4835658
Median Absolute Deviation (MAD)0.832
Skewness-0.053811479
Sum12957.666
Variance1.2667987
MonotonicityNot monotonic
2024-12-11T22:57:32.143970image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.252 5
 
0.2%
5.936 4
 
0.2%
5.104 4
 
0.2%
5.304 4
 
0.2%
5.057 4
 
0.2%
5.959 4
 
0.2%
4.609 4
 
0.2%
5.887 4
 
0.2%
5.311 4
 
0.2%
6.375 4
 
0.2%
Other values (1804) 2322
98.3%
ValueCountFrequency (%)
1.281 1
< 0.1%
1.446 1
< 0.1%
2.179 1
< 0.1%
2.352 1
< 0.1%
2.375 1
< 0.1%
2.436 1
< 0.1%
2.56 1
< 0.1%
2.634 1
< 0.1%
2.662 1
< 0.1%
2.688 1
< 0.1%
ValueCountFrequency (%)
8.019 1
< 0.1%
7.971 1
< 0.1%
7.889 1
< 0.1%
7.858 1
< 0.1%
7.834 1
< 0.1%
7.794 1
< 0.1%
7.788 2
0.1%
7.78 1
< 0.1%
7.776 1
< 0.1%
7.771 1
< 0.1%

Log GDP per capita
Real number (ℝ)

High correlation  Missing 

Distinct1760
Distinct (%)75.4%
Missing28
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean9.3996711
Minimum5.527
Maximum11.676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:32.376447image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum5.527
5-th percentile7.3657
Q18.5065
median9.503
Q310.3925
95-th percentile10.9396
Maximum11.676
Range6.149
Interquartile range (IQR)1.886

Descriptive statistics

Standard deviation1.1520694
Coefficient of variation (CV)0.12256487
Kurtosis-0.77245373
Mean9.3996711
Median Absolute Deviation (MAD)0.941
Skewness-0.33668491
Sum21948.232
Variance1.327264
MonotonicityNot monotonic
2024-12-11T22:57:32.662122image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.878 6
 
0.3%
10.637 5
 
0.2%
9.381 5
 
0.2%
9.283 5
 
0.2%
10.799 4
 
0.2%
10.856 4
 
0.2%
10.794 4
 
0.2%
10.002 4
 
0.2%
9.383 4
 
0.2%
10.714 4
 
0.2%
Other values (1750) 2290
96.9%
(Missing) 28
 
1.2%
ValueCountFrequency (%)
5.527 1
< 0.1%
5.935 1
< 0.1%
5.943 1
< 0.1%
6.607 1
< 0.1%
6.687 1
< 0.1%
6.694 1
< 0.1%
6.699 1
< 0.1%
6.7 1
< 0.1%
6.707 1
< 0.1%
6.723 1
< 0.1%
ValueCountFrequency (%)
11.676 1
< 0.1%
11.664 1
< 0.1%
11.657 1
< 0.1%
11.653 1
< 0.1%
11.649 2
0.1%
11.647 1
< 0.1%
11.645 1
< 0.1%
11.643 1
< 0.1%
11.638 1
< 0.1%
11.637 1
< 0.1%

Social support
Real number (ℝ)

High correlation 

Distinct484
Distinct (%)20.6%
Missing13
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.80936936
Minimum0.228
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:32.993146image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.228
5-th percentile0.56445
Q10.744
median0.8345
Q30.904
95-th percentile0.95055
Maximum0.987
Range0.759
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.12121176
Coefficient of variation (CV)0.14976075
Kurtosis1.1317767
Mean0.80936936
Median Absolute Deviation (MAD)0.0755
Skewness-1.1092983
Sum1902.018
Variance0.014692292
MonotonicityNot monotonic
2024-12-11T22:57:33.231403image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.937 17
 
0.7%
0.909 16
 
0.7%
0.818 16
 
0.7%
0.878 15
 
0.6%
0.904 15
 
0.6%
0.896 15
 
0.6%
0.917 15
 
0.6%
0.866 15
 
0.6%
0.856 14
 
0.6%
0.954 14
 
0.6%
Other values (474) 2198
93.0%
ValueCountFrequency (%)
0.228 1
< 0.1%
0.29 1
< 0.1%
0.291 2
0.1%
0.303 1
< 0.1%
0.32 1
< 0.1%
0.326 1
< 0.1%
0.366 1
< 0.1%
0.368 1
< 0.1%
0.373 1
< 0.1%
0.382 1
< 0.1%
ValueCountFrequency (%)
0.987 1
 
< 0.1%
0.985 2
0.1%
0.984 1
 
< 0.1%
0.983 2
0.1%
0.982 2
0.1%
0.98 2
0.1%
0.979 3
0.1%
0.977 2
0.1%
0.976 1
 
< 0.1%
0.975 1
 
< 0.1%

Healthy life expectancy at birth
Real number (ℝ)

High correlation  Missing 

Distinct1126
Distinct (%)49.0%
Missing63
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean63.401828
Minimum6.72
Maximum74.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:33.406194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum6.72
5-th percentile51.119
Q159.195
median65.1
Q368.5525
95-th percentile71.65125
Maximum74.6
Range67.88
Interquartile range (IQR)9.3575

Descriptive statistics

Standard deviation6.8426444
Coefficient of variation (CV)0.10792503
Kurtosis2.930414
Mean63.401828
Median Absolute Deviation (MAD)4.36
Skewness-1.1298193
Sum145824.2
Variance46.821782
MonotonicityNot monotonic
2024-12-11T22:57:33.661752image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.6 19
 
0.8%
70 18
 
0.8%
65.8 18
 
0.8%
67.5 13
 
0.6%
71.4 12
 
0.5%
67 12
 
0.5%
65.7 12
 
0.5%
67.6 11
 
0.5%
66.3 11
 
0.5%
71.5 10
 
0.4%
Other values (1116) 2164
91.6%
(Missing) 63
 
2.7%
ValueCountFrequency (%)
6.72 1
< 0.1%
17.36 1
< 0.1%
28 1
< 0.1%
33.32 1
< 0.1%
38.64 1
< 0.1%
40.4 1
< 0.1%
41.48 1
< 0.1%
41.52 1
< 0.1%
41.6 1
< 0.1%
42.25 1
< 0.1%
ValueCountFrequency (%)
74.6 1
< 0.1%
74.475 1
< 0.1%
74.35 1
< 0.1%
74.225 1
< 0.1%
74.2 1
< 0.1%
74.1 1
< 0.1%
74 1
< 0.1%
73.975 1
< 0.1%
73.925 1
< 0.1%
73.9 1
< 0.1%

Freedom to make life choices
Real number (ℝ)

High correlation  Missing 

Distinct550
Distinct (%)23.6%
Missing36
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.75028191
Minimum0.228
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:33.930696image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.228
5-th percentile0.484
Q10.661
median0.771
Q30.862
95-th percentile0.936
Maximum0.985
Range0.757
Interquartile range (IQR)0.201

Descriptive statistics

Standard deviation0.13935703
Coefficient of variation (CV)0.18573956
Kurtosis0.052537831
Mean0.75028191
Median Absolute Deviation (MAD)0.099
Skewness-0.69988127
Sum1745.906
Variance0.019420383
MonotonicityNot monotonic
2024-12-11T22:57:34.223575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.891 13
 
0.6%
0.838 13
 
0.6%
0.904 12
 
0.5%
0.905 11
 
0.5%
0.8 11
 
0.5%
0.757 11
 
0.5%
0.878 11
 
0.5%
0.772 11
 
0.5%
0.817 11
 
0.5%
0.776 11
 
0.5%
Other values (540) 2212
93.6%
(Missing) 36
 
1.5%
ValueCountFrequency (%)
0.228 1
< 0.1%
0.258 1
< 0.1%
0.26 1
< 0.1%
0.281 1
< 0.1%
0.287 1
< 0.1%
0.295 1
< 0.1%
0.304 1
< 0.1%
0.306 1
< 0.1%
0.315 1
< 0.1%
0.332 1
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.98 1
 
< 0.1%
0.975 1
 
< 0.1%
0.971 1
 
< 0.1%
0.97 3
0.1%
0.969 2
0.1%
0.968 1
 
< 0.1%
0.965 3
0.1%
0.964 2
0.1%

Generosity
Real number (ℝ)

Missing 

Distinct650
Distinct (%)28.5%
Missing81
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean9.7721297 × 10-5
Minimum-0.34
Maximum0.7
Zeros10
Zeros (%)0.4%
Negative1270
Negative (%)53.7%
Memory size18.6 KiB
2024-12-11T22:57:34.503429image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-0.34
5-th percentile-0.23095
Q1-0.112
median-0.022
Q30.09375
95-th percentile0.295
Maximum0.7
Range1.04
Interquartile range (IQR)0.20575

Descriptive statistics

Standard deviation0.1613876
Coefficient of variation (CV)1651.509
Kurtosis0.83319907
Mean9.7721297 × 10-5
Median Absolute Deviation (MAD)0.103
Skewness0.76938063
Sum0.223
Variance0.026045958
MonotonicityNot monotonic
2024-12-11T22:57:34.783720image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.068 14
 
0.6%
-0.004 11
 
0.5%
-0.059 11
 
0.5%
-0.131 11
 
0.5%
-0.107 11
 
0.5%
-0.054 11
 
0.5%
-0.027 11
 
0.5%
-0.106 11
 
0.5%
-0.084 11
 
0.5%
-0.06 11
 
0.5%
Other values (640) 2169
91.8%
(Missing) 81
 
3.4%
ValueCountFrequency (%)
-0.34 1
< 0.1%
-0.321 1
< 0.1%
-0.318 1
< 0.1%
-0.312 1
< 0.1%
-0.311 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.302 1
< 0.1%
-0.301 1
< 0.1%
ValueCountFrequency (%)
0.7 1
< 0.1%
0.692 1
< 0.1%
0.691 1
< 0.1%
0.68 1
< 0.1%
0.651 1
< 0.1%
0.646 1
< 0.1%
0.6 1
< 0.1%
0.59 1
< 0.1%
0.56 1
< 0.1%
0.549 1
< 0.1%

Perceptions of corruption
Real number (ℝ)

Missing 

Distinct613
Distinct (%)27.4%
Missing125
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean0.74397096
Minimum0.035
Maximum0.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:35.036112image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.3157
Q10.687
median0.7985
Q30.86775
95-th percentile0.93815
Maximum0.983
Range0.948
Interquartile range (IQR)0.18075

Descriptive statistics

Standard deviation0.18486548
Coefficient of variation (CV)0.24848481
Kurtosis1.8103484
Mean0.74397096
Median Absolute Deviation (MAD)0.0855
Skewness-1.4855755
Sum1665.007
Variance0.034175246
MonotonicityNot monotonic
2024-12-11T22:57:35.489893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.844 16
 
0.7%
0.755 14
 
0.6%
0.841 14
 
0.6%
0.884 14
 
0.6%
0.743 13
 
0.6%
0.868 13
 
0.6%
0.855 13
 
0.6%
0.848 12
 
0.5%
0.849 12
 
0.5%
0.812 12
 
0.5%
Other values (603) 2105
89.1%
(Missing) 125
 
5.3%
ValueCountFrequency (%)
0.035 1
< 0.1%
0.047 1
< 0.1%
0.06 1
< 0.1%
0.064 1
< 0.1%
0.066 1
< 0.1%
0.07 1
< 0.1%
0.078 1
< 0.1%
0.081 1
< 0.1%
0.095 1
< 0.1%
0.097 1
< 0.1%
ValueCountFrequency (%)
0.983 2
0.1%
0.979 1
 
< 0.1%
0.977 2
0.1%
0.976 2
0.1%
0.974 1
 
< 0.1%
0.973 2
0.1%
0.97 2
0.1%
0.969 1
 
< 0.1%
0.968 3
0.1%
0.967 3
0.1%

Positive affect
Real number (ℝ)

High correlation  Missing 

Distinct442
Distinct (%)18.9%
Missing24
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.651882
Minimum0.179
Maximum0.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:35.760327image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.179
5-th percentile0.474
Q10.572
median0.663
Q30.737
95-th percentile0.803
Maximum0.884
Range0.705
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.1062397
Coefficient of variation (CV)0.16297383
Kurtosis-0.15236622
Mean0.651882
Median Absolute Deviation (MAD)0.08
Skewness-0.45893643
Sum1524.752
Variance0.011286875
MonotonicityNot monotonic
2024-12-11T22:57:36.055176image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.718 16
 
0.7%
0.699 15
 
0.6%
0.583 13
 
0.6%
0.71 13
 
0.6%
0.74 13
 
0.6%
0.742 13
 
0.6%
0.702 13
 
0.6%
0.689 13
 
0.6%
0.717 13
 
0.6%
0.745 12
 
0.5%
Other values (432) 2205
93.3%
(Missing) 24
 
1.0%
ValueCountFrequency (%)
0.179 1
< 0.1%
0.206 1
< 0.1%
0.261 1
< 0.1%
0.263 1
< 0.1%
0.297 1
< 0.1%
0.298 1
< 0.1%
0.308 1
< 0.1%
0.311 1
< 0.1%
0.324 1
< 0.1%
0.332 1
< 0.1%
ValueCountFrequency (%)
0.884 1
< 0.1%
0.876 1
< 0.1%
0.874 1
< 0.1%
0.86 1
< 0.1%
0.853 1
< 0.1%
0.851 1
< 0.1%
0.849 1
< 0.1%
0.847 1
< 0.1%
0.844 1
< 0.1%
0.843 1
< 0.1%

Negative affect
Real number (ℝ)

Distinct394
Distinct (%)16.8%
Missing16
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.27315083
Minimum0.083
Maximum0.705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 KiB
2024-12-11T22:57:36.351981image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.083
5-th percentile0.152
Q10.209
median0.262
Q30.326
95-th percentile0.431
Maximum0.705
Range0.622
Interquartile range (IQR)0.117

Descriptive statistics

Standard deviation0.087131072
Coefficient of variation (CV)0.3189852
Kurtosis0.63533641
Mean0.27315083
Median Absolute Deviation (MAD)0.057
Skewness0.69807675
Sum641.085
Variance0.0075918238
MonotonicityNot monotonic
2024-12-11T22:57:36.656374image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.206 20
 
0.8%
0.24 19
 
0.8%
0.233 16
 
0.7%
0.231 16
 
0.7%
0.232 16
 
0.7%
0.285 16
 
0.7%
0.245 15
 
0.6%
0.243 15
 
0.6%
0.209 15
 
0.6%
0.26 15
 
0.6%
Other values (384) 2184
92.4%
(Missing) 16
 
0.7%
ValueCountFrequency (%)
0.083 2
0.1%
0.093 2
0.1%
0.094 1
 
< 0.1%
0.095 2
0.1%
0.1 1
 
< 0.1%
0.103 1
 
< 0.1%
0.106 1
 
< 0.1%
0.107 1
 
< 0.1%
0.108 3
0.1%
0.109 1
 
< 0.1%
ValueCountFrequency (%)
0.705 1
< 0.1%
0.643 1
< 0.1%
0.622 1
< 0.1%
0.607 1
< 0.1%
0.599 1
< 0.1%
0.591 1
< 0.1%
0.581 1
< 0.1%
0.576 1
< 0.1%
0.57 1
< 0.1%
0.569 1
< 0.1%

Interactions

2024-12-11T22:57:26.480387image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:07.336456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.915487image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.302110image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.670919image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:14.593013image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:17.288726image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:19.496668image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:21.746610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:24.050139image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:26.735433image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:07.486258image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.087660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.439155image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.803267image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:14.887388image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:17.541719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:19.959943image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:21.951148image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:24.241401image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:27.039973image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:07.698807image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.237893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.576898image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.935575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:15.237968image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:17.765613image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:20.180378image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:22.149104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:24.439301image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:27.254042image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:07.828872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.369659image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.702690image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:12.186868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:15.504780image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:17.982833image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:20.332844image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:22.331389image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:24.668761image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:27.484778image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:07.962596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.511509image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.878207image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:12.493238image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:15.800922image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:18.183444image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:20.560287image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:22.584075image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:24.896669image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:27.726794image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.102379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.635875image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.003513image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:12.790036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:16.103693image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:18.392196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:20.726610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:22.832361image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:25.140785image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:27.954377image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.241379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.765036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.153762image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:13.092855image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:16.427042image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:18.658949image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:20.914861image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:23.053386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:25.365943image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:28.177893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.378852image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:09.910686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.268550image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:13.703833image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:16.676980image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:18.885321image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:21.100395image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:23.317929image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:25.569293image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:28.382154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.505227image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.038575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.386261image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:14.037874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:16.861146image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:19.124858image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:21.298738image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:23.598647image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:25.833393image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:28.646517image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:08.778789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:10.172428image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:11.536290image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:14.302743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:17.066021image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:19.282868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:21.513672image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:23.833558image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T22:57:26.048445image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-11T22:57:36.928501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Freedom to make life choicesGenerosityHealthy life expectancy at birthLife LadderLog GDP per capitaNegative affectPerceptions of corruptionPositive affectSocial supportyear
Freedom to make life choices1.0000.3480.4040.5490.401-0.263-0.4600.5760.4450.227
Generosity0.3481.0000.0350.1650.012-0.081-0.2280.2990.0860.039
Healthy life expectancy at birth0.4040.0351.0000.7650.853-0.163-0.2450.2590.6510.153
Life Ladder0.5490.1650.7651.0000.803-0.315-0.3300.5110.7600.069
Log GDP per capita0.4010.0120.8530.8031.000-0.269-0.2730.2500.7240.083
Negative affect-0.263-0.081-0.163-0.315-0.2691.0000.204-0.287-0.4440.207
Perceptions of corruption-0.460-0.228-0.245-0.330-0.2730.2041.000-0.271-0.204-0.127
Positive affect0.5760.2990.2590.5110.250-0.287-0.2711.0000.4040.024
Social support0.4450.0860.6510.7600.724-0.444-0.2040.4041.000-0.034
year0.2270.0390.1530.0690.0830.207-0.1270.024-0.0341.000

Missing values

2024-12-11T22:57:28.935170image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-11T22:57:29.168752image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-11T22:57:29.607518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country nameyearLife LadderLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
0Afghanistan20083.7247.3500.45150.5000.7180.1640.8820.4140.258
1Afghanistan20094.4027.5090.55250.8000.6790.1870.8500.4810.237
2Afghanistan20104.7587.6140.53951.1000.6000.1180.7070.5170.275
3Afghanistan20113.8327.5810.52151.4000.4960.1600.7310.4800.267
4Afghanistan20123.7837.6610.52151.7000.5310.2340.7760.6140.268
5Afghanistan20133.5727.6800.48452.0000.5780.0590.8230.5470.273
6Afghanistan20143.1317.6710.52652.3000.5090.1020.8710.4920.375
7Afghanistan20153.9837.6540.52952.6000.3890.0780.8810.4910.339
8Afghanistan20164.2207.6500.55952.9250.5230.0400.7930.5010.348
9Afghanistan20172.6627.6480.49153.2500.427-0.1230.9540.4350.371
Country nameyearLife LadderLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
2353Zimbabwe20144.1847.7480.76650.0000.642-0.0620.8200.6610.239
2354Zimbabwe20153.7037.7470.73651.2000.667-0.1110.8100.6390.179
2355Zimbabwe20163.7357.7350.76851.6750.733-0.0820.7240.6850.209
2356Zimbabwe20173.6387.7540.75452.1500.753-0.0840.7510.7340.224
2357Zimbabwe20183.6167.7830.77552.6250.763-0.0550.8440.6580.212
2358Zimbabwe20192.6947.6980.75953.1000.632-0.0510.8310.6580.235
2359Zimbabwe20203.1607.5960.71753.5750.6430.0030.7890.6610.346
2360Zimbabwe20213.1557.6570.68554.0500.668-0.0790.7570.6100.242
2361Zimbabwe20223.2967.6700.66654.5250.652-0.0730.7530.6410.191
2362Zimbabwe20233.5727.6790.69455.0000.735-0.0690.7570.6100.179